The neurophysiological effect of NMDA-R antagonism of frontotemporal lobar degeneration is conditional on individual GABA concentration

There is a pressing need to accelerate therapeutic strategies against the syndromes caused by frontotemporal lobar degeneration, including symptomatic treatments. One approach is for experimental medicine, coupling neurophysiological studies of the mechanisms of disease with pharmacological interventions aimed at restoring neurochemical deficits. Here we consider the role of glutamatergic deficits and their potential as targets for treatment. We performed a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study in 20 people with symptomatic frontotemporal lobar degeneration (10 behavioural variant frontotemporal dementia, 10 progressive supranuclear palsy) and 19 healthy age- and gender-matched controls. Both magnetoencephalography sessions recorded a roving auditory oddball paradigm: on placebo or following 10 mg memantine, an uncompetitive NMDA-receptor antagonist. Ultra-high-field magnetic resonance spectroscopy confirmed lower concentrations of GABA in the right inferior frontal gyrus of people with frontotemporal lobar degeneration. While memantine showed a subtle effect on early-auditory processing in patients, there was no significant main effect of memantine on the magnitude of the mismatch negativity (MMN) response in the right frontotemporal cortex in patients or controls. However, the change in the right auditory cortex MMN response to memantine (vs. placebo) in patients correlated with individuals’ prefrontal GABA concentration. There was no moderating effect of glutamate concentration or cortical atrophy. This proof-of-concept study demonstrates the potential for baseline dependency in the pharmacological restoration of neurotransmitter deficits to influence cognitive neurophysiology in neurodegenerative disease. With changes to multiple neurotransmitters in frontotemporal lobar degeneration, we suggest that individuals’ balance of excitation and inhibition may determine drug efficacy, with implications for drug selection and patient stratification in future clinical trials.


Auditory roving oddball paradigm:
MEG was recorded while participants completed an auditory roving oddball paradigm [1,2]. Binaural sinusoidal tones (60dB above the population average threshold) were presented for 75ms to participants through headphones, with a 7.5ms ramp up and down at the start and end of the tone, and with stimulus onset asynchrony of 500ms (ITI). This roving oddball paradigm comprises mini-blocks of 3-10 tone repetitions, where at the end of each mini-block the tone frequency changed pseudorandomly. This first tone of each block changing in frequency is defined as the deviant (dev) tone. Tone frequencies presented were in the range of 400-800Hz. Participants were under continuous video monitoring to ensure none fell asleep, and they were not asked to attend to the auditory stimuli. The paradigm was performed eyes-open in blocks of five minutes while participants watched a movie (i.e. walking with dinosaurs), with an average number of 1577 (SD=109) stimulus trials (before trial rejection) across each subject and session.

MEG data acquisition and preprocessing:
MEG data were acquired in a magnetically-shielded (IMEDCO) room using the Elekta VectorView system (Elekta Neuromag, Helsinki). This MEG system comprises of 306-channel recordings at 102 spatial locations (a pair of planar gradiometers and a magnetometer at each site) and was sampled at 1000 Hz, with a high-pass filter of 0.03 Hz. Electroocculograms (EOGs) tracked eye movements vertically and horizontally and 5 head position indicator coils tracked head position every 200ms. A 70 channel, MEG-compatible, electroencephalogram (EEG) cap (Easycap GmbH) using Ag/AgCl electrodes positioned according to the 10-20 system was used concurrently, although this modality was not used for rejecting bad trials, source reconstruction, nor in subsequent analysis. Scalp shape was recorded with a 3D digitizer (Fastrak Polhemus Inc., Colchester, VA) using > 100 scalp points, as well as the position of the nasion and bilateral pre-auricular fiducial points. Auditory stimuli were delivered binaurally through MEG-compatible ER3A insert earphones (Etymotic Research). Instructions and the video were presented on the screen positioned 1.22 meters in front of the participant's visual field.
To ensure that the earphones were working correctly, before the MEG recording participants performed an automated hearing test in the scanner. They were presented tones at 1000Hz to either ear with varying loudness, and instructed to press the button when they heard the tone.
First, the raw E/MEG were preprocessed using MaxFilter 2.2.12 in Matlab 2018a (Elekta Neuromag, https://imaging.mrc-cbu.cam.ac.uk/meg/Maxfilter_V2.2). This included interpolation of bad channels, signal source separation to remove noisy signals from outside the brain, and head motion correction. Subject data was also transformed into a standard space for the analysis of sensor channels.
Next, the data were downsampled to 500Hz, band-pass filtered (0.1-125 Hz using a fifth-order Butterworth filter) and further notch filtered to remove 50 (45-55Hz) and 100 Hz (95-105Hz) line noise [4]. Artefact rejection (osl_detect_artefacts) was first used to remove bad channels, and then independent component analysis (ICA) further removed eye-movement related artefacts. ICA involved: A fast fixed-point algorithm, 800 maximum steps, 60 principal components, symmetric approach, tan-h non-linearity, epsilon of 0.00001, via the FastICA package for MATLAB. The independent component time series were correlated with the VEOG and HEOG channel time series. The components that revealed correlations higher than r = 0.35 were removed and the data of the remaining independent components were reconstructed. Continuous data were epoched between -100 to 400ms relative to auditory stimulus presentation, and bad trials were identified and removed again using OSL's artefact rejection (using magnetometers and gradiometers). Artifact MEG channels and trials were both removed using osl_detect_artefacts, which identifies outliers through a Generalized Extreme Studentized Deviate (ESD) test, at the default significant threshold of α=0.05. Lastly, trials were averaged using robust averaging and then again low-pass filter corrected (125 Hz) to remove high-frequency noise induced after averaging.

Independent control cohort
We used the MEG data from an independent control cohort [2] to (1) corroborate calculation of individual MMN responses based upon the rep3-dev waveform, and (2) determine that our reported group differences (to FTLD patients) at the sensor level are robust to the control cohort studied. This control population is previously published, and do not differ in age (p=0.69) to the current control cohort.
The MEG data were acquired, preprocessed, and source localised identically to the steps performed in the current control cohort. The only difference is that while for the current cohort T1-weighted images were acquired at 7T, this independent cohort underwent structural MRI (MPRAGE sequence, TE = 2.9ms, TR = 2000ms, 1.1 mm isotropic voxels) using a different 3T Siemens PRISMA scanner.
All structural images were visually inspected for motion and scanning artifacts, as well as segmentation accuracy.

Acquisition and preprocessing of MR Spectroscopy
Magnetic resonance spectra data were acquired in controls and patients as part of a larger cohort study [5]. MRS data were acquired serially from a region-of-interest in the right inferior frontal gyrus (2 x 2 x 2 cm 3 ). Voxel selection was performed manually using anatomical landmarks from the T1-weighted structural scan, with Murley et al. [5] (c.f. Fig. 1) demonstrating the consistency of voxel placement across subjects (Fig. 1B in the current study). A control region in the right primary visual cortex was also included. Spectra were acquired using a short-echo semi-LASER sequence [6,7] (repetition time/echo time = 5000/26 ms, 64 repetitions) and we used the recommended pre-scan protocol of FASTESTMAP shimming [8], semi-LASER water-peak flip angle, and VAPOR water suppression calibration [9].
For every participant, each of the 64 individual spectral transients from each participant were saved separately. Eddy-current effects were corrected for and also frequency and phase shifts using MRspa (Dinesh Deelchand, University of Minnesota, www.cmrr.umn.edu/downloads/mrspa). Neurochemicals between 0.5 and 4.2ppm, including glutamate and GABA, were quantified using LCModel (Version 6.2-3) [10] with water scaling and a simulated basis set that included experimentally-acquired macromolecule spectra. See Fig.1 from [5] for illustration of the MRS Spectrum (from all participants) and LCModel fit for GABA and Glutamate in the inferior frontal gyrus.
Grey matter volume was used to correct for GABA, and grey and white matter volume for glutamate. A generalized linear model (GLM) removed the effect of age, sex, and partial volume [5]. The model was weighted by Cramér-Rao lower bound values (CRLB, SI Table 2), so that participants with less accurate metabolite estimates were penalised to have less influence on the regression. Non-corrected MRS values are also presented.

Preprocessing and segmentation of anatomical images
For those participants with MP2RAGE images, we first performed signal bias correction [11] and segmentation of grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) segments using the standard settings in SPM12 (v7771). Next, we created a study-specific template image with differomorphic registration (DARTEL) [12] of native space grey and white matter images from an equal number of control, bvFTD and PSP individuals. Each participant's native GM image were then normalised to standard space by applying the deformation combined with the affine transformation parameters (i.e. native-space to group average to MNI space) which included modulation in order to preserve local volume. The images were lastly smoothed with a Gaussian kernel at 8 mm full width half maximum (FWHM). The total intracranial volume (TIV) for each participant was calculated using the Tissue Volumes function in SPM12.

Association between prefrontal cortical atrophy and change in MMN response to drug
Grey matter volume (GMV) in the inferior frontal gyrus was calculated from a right-hemisphere anatomical mask combining Brodmann areas 8, 9 and the frontal operculum (OP8) (https://github.com/inm7/jubrain-anatomy-toolbox, v2d7a002) [13], regions which overlapped with the MMN sphere used in MEG analysis (SI Fig. 9A, and is available at https://neurovault.org/images/776918). GMV was calculated from the total sum of voxel probabilities within the above mask enclosing the individual normalized grey-matter images. The normalization included modulation in order to preserve local volume, but were not smoothed.
A linear model was performed to test for the association between the GMV in the inferior frontal gyrus (IV) with the dependent variable, the change in MMN response to memantine (vs. placebo) in the auditory cortex. Total intracranial volume and age were included as covariates in the linear model. Bayesian linear regression was also conducted.

Voxel-based morphometry
Voxel-based morphometry (VBM) was used in SPM12 to compare GM volume between controls and patients (bvFTD and PSP combined) and across the diagnostic groups. GM volumes for each diagnostic group were compared with independent two-sample t-tests with age, sex and total intracranial volume used as covariates of no interest [14]. Significant effects were identified using cluster-level statistics (p<0.05, family-wise error corrected for multiple comparisons) above a height threshold of p<0.001 (uncorrected). Unthresholded statistical maps for each contrast are available at https://neurovault.org/collections/12279. Table 1

. Demographic and neuropsychological characteristics of participants by disease subgroup
Attached as separate file SI